Using Ontology Dimensions and Negative Expansion to solve Precise Queries in the ImageCLEF Medical Task
Conference and Labs of the Evaluation Forum
We present here the method we have used for indexing multilingual text part of the Image Medical CLEF Collection. The result of the textual querying is then mixed with the image matching. We show by our results that a fusion of two media are of a great benefice because the combination of text and image returns clear better results than the two separately. We focus in this paper on the textual indexing part using a medical ontology to filter the document collection. At first, we use the notion
... ontology dimensions, which corresponds the split of the ontology into sub ontology. In our experiment we just use the first tree level of the MESH ontology. We have modelled and experimented two different approaches of the use of the ontology: the first one is an ontology filtering that can force some terms of one dimension to be present in the final document. We have noticed a strong improvement using this technique over the classic Vector Space Model. The second technique manages the preference of some terms among other in the same dimension. Our hypothesis is that precise document should emphasis only few terms of a given dimension. To compute this new constraint, we have set up a negative weight query expansion. Finally, the combination of the two methods produces the overall best results. To our opinion, it shows that for a given domain, adding explicit knowledge stored into an ontology tree, enable to classify the importance of terms used in the query and enhance the finale average precision.